See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/228519767 A comparison of scheduling algorithms in HSDPA Article CITATION READS 1 143 2 authors: Stefan M Scriba Fambirai Takawira University of KwaZulu-Natal University of the Witwatersrand 3 PUBLICATIONS 2 CITATIONS 180 PUBLICATIONS 877 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: Blind channel Estimation schemes View project Optical OFDM Systems View project All content following this page was uploaded by Fambirai Takawira on 28 May 2014. The user has requested enhancement of the downloaded file. SEE PROFILE A comparison of scheduling algorithms in HSDPA Stefan M. Scriba and Fambirai Takawira School of Electrical, Electronic and Computer Engineering, University of KwaZulu-Natal King George V Avenue, Durban, 4041, South Africa E-mail: scribas@gmail.com or ftakaw@ukzn.ac.za Tel: +27-31-260-2730 Abstract—This paper compares the behaviour of Earliest Deadline First (EDF) and Opportunistic-EDF (O-EDF) with that of Round Robin, Proportional Fair Throughput (PFT), and Maximum Carrier to Interference ratio (MaxC/I) . Video, voice and web traffic are transmitted through an HSDPA air interface. The resultant queueing delay and fairness are measured and compared for each scheduler. Index Terms—Earliest Deadline First (EDF), queueing delay. I. I NTRODUCTION As part of the UMTS standard, the third generation partnership project (3GPP) included High Speed Downlink Packet Access (HSDPA) in Release 5. In HSDPA, all resources are usually made available to a single user on a Transmission Time Interval (TTI) basis [1]. HSDPA distinguishes itself from the previous WCDMA standard, by using an Adaptive Modulation and Coding (AMC) scheme, which enables it to respond rapidly to channel fluctuations, without the need for fast power control, which is disabled in HSDPA. Furthermore, Variable Spreading Factors have also been disabled, while the TTI has been decreased from 10ms to 2ms, allowing for much faster scheduling responses. Finally, the model makes use of a Fast Physical Layer Hybrid ARQ scheme. This paper compares the behaviour of EDF and Opportunistic-EDF (O-EDF) with that of Round Robin, PFT, and MaxC/I. Video, voice and web traffic are transmitted through an HSDPA air interface. This is achieved by means of a custom built simulator, which shows how the Earliest Deadline First (EDF) scheduler fills an essential role in providing QoS for delay sensitive traffic. II. T RAFFIC GENERATION MODEL The simulation model assumes that multiple sources are producing voice, video, and web traffic that will arrive at the base station, enter the queueing system and is then transmitted via a channel to the mobile stations. Note that each physical mobile unit can simultaneously generate voice, video and web traffic, in other words, it can consist of several traffic sources. The same traffic generating models for video and web traffic will be used as [1], while the video generating model will be extended to give a realistic voice generating model. The scheduling intervals are defined in terms of the Transmission This work was partially sponsored by Alcatel-Lucent and Telkom SA Ltd as part of the Centre of Excellence Programme. Time Interval (TTI). In other words, a new packet is scheduled every TTI. In an HSDPA system, the TTI is fixed at 2ms. A. Video traffic source A video source can be modeled as M independent ONOFF Markov mini-sources. As was the case in [1], this model assumes that M = 10. In the ON state, a mini-source produces a constant rate of V bps, while in the OFF state, a mini-source produces no traffic. Each mini-source spends a mean time of p TTIs in the ON state and q TTIs in the OFF state, where their respective random variables P and Q are geometrically distributed. Parameters p, q and V are obtained as follows: µ ¶ µ2 1 1+ p= a · TTI M σ2 µ ¶ 1 M σ2 q= 1+ 2 a · TTI µ 2 σ µ + [bps] V = M µ [TTIs] (1) [TTIs] (2) (3) A video source has a mean bit rate of µbps, a standard deviation of σbps, and exponent of the auto-covariance with coefficient as−1 . As was the case in [1], µ=128kbps, σ=8kbps and a=3.9s−1 . Video packets are chosen to have a fixed packet length of 1500 Bytes = 12kb. Video traffic is considered to have a deadline of d=100ms, after which it will be dropped. The result is that the mean ON-time is p = 3410.26 TTI = 6, 820, 513 µs (4) Similarly, the mean OFF-time is q = 133.21 TTI = 266, 420 µs (5) And finally, the transmission rate of each mini-source is V = 13, 300 bps (6) B. Voice traffic source For the voice traffic generation, exactly the same model is used as for video traffic generation, except that M =1. In other words, every source no longer consists of 10 mini-sources. For voice traffic, µ=64,000bps and σ=4,000bps. Voice packets have a fixed packet length of 80 Bytes = 640b. Voice traffic has a deadline of d=50ms, after which it will be dropped. Once again, the mean ON-time is calculated to be p = 32, 948.7 TTI = 65, 897, 436 µs (7) Pareto distributed random numbers can be obtained, by generating random numbers for a uniform distribution with limits [0,1]. For every uniformly distributed random u generated in this fashion, F (x) = u. One may then solve for x, as follows: Similarly, the mean OFF-time is given by x= q = 128.71 TTI = 257, 412 µs (8) And finally, the source transmission rate is V = 64, 250 bps Although it is fairly difficult to find a statistical distribution that accurately describes web traffic generation patterns, creating simulated web-traffic is fairly straight forward. We use the same model as in [1], where an end-user can be seen to oscillate between two states while browsing. He is either requesting a new webpage or reading. The reading state has a geometric distribution with an average duration of TOF F =1000TTIs = 2s. During this time no traffic is generated. Web traffic has a virtual deadline of d=500ms. It is virtual because the packet is still served and not dropped when the delay exceeds d and creates a deadline violation. When in the request state, the addressed data source will produce a geometrically distributed number of packets, with a mean of P̄ =300 packets. The packet inter-arrival time too is geometrically distributed, with a mean of △T =200TTIs = 0.4s. The packet length L can be found by finding the floor of a truncated Pareto pdf, in other words, L = ⌊x⌋, where: p(x) = ζ · lmin ζ [u(x − lmin ) − u(x − lmax )] xζ+1 µ ¶ζ lmin + δ(x − lmax ) [bits] lmax (10) Here ζ = 1.1 is a constant, u(·) is the unitary step function, δ(·) is the Dirac Delta function, while lmin = 80 Bytes = 640b and lmax = 1500 Bytes = 12kb are respectively the minimum and maximum message lengths. The values of lmin and lmax are different to those proposed in [1] and were chosen to lie within the bounds of the packet lengths of voice and video packets. The problem with generating web traffic is that the IMSL library that was used to perform statistical tasks does not include a function for generating Pareto distributed random numbers. To solve this problem, it was noted that the Pareto density function is given by p(x) = aba xa+1 [bits], (11) To be able to compare all schedulers, identical conditions must be created in each case. The only difference may be the way the data is scheduled, in other words, the order in which it is transmitted. As described earlier, traffic that has violated its delay deadline is either physically dropped or virtually dropped, depending on whether it is real-time traffic (voice and video) or best-effort traffic (web). Because the real-time traffic can be dropped, its queue-length is indirectly limited to the product of the average service rate and the delay deadline. Under high enough load conditions, the best-effort queue could, on the other hand, grow to a significant size, which in turn drastically affects its delay behaviour. Limiting the queue size in any way would also indirectly cap the possible maximum delay. The result is that infinitely long buffers were chosen for all queues. IV. C HANNEL MODEL HSDPA uses Direct-Sequence Code Division Multiple Access (DS-CDMA). The bit energy-to-interference spectral density EI0b that the User Equipment (UE) measures is fed back to Node-B using 5 bits, known as the Channel Quality Indicator (CQI) value. The CQI value is used to decide on the highest transmission rate that can be chosen. The EI0b value that the UE measures, can be modeled as follows [1]: Eb = SIR · PG, I0 F (x) = 1 − lmin x ¶ζ [bits]. (12) (14) where SIR is the signal-to-interference power ratio, PG is the DSSS Processing Gain, which is defined by the ratio W Rb , where W is the spreading bandwidth and Rb is the bitrate that depends on the used modulation and coding scheme (MCS). The SIR value can be estimated as SIR = PRX , IInter + IExtra + N (15) where PRX is the useful received power, IInter is the intracell interference, IExtra is the extra-cell interference and N is the noise power. Note that PRX , IInter , IExtra and N can be characterized as follows [1]: IExtra = µ (13) III. Q UEUEING MODEL RX , PRX = PT X GP c while the Pareto cumulative distribution is given by [bits]. The result is that one is able to translate every uniformly distributed random number u into a Pareto distributed random number x. (9) C. Web traffic source lmin (1 − u)1/ζ εGIcExtra PD , Inter PD , IInter = αGP c N = N0 W (16) (17) RX , where PT X is the transmitted power to the user, GP c IExtra Inter are the user channel gains of the , and G GP c c transmission, the intra-cell interference, and the extra-cell interference, respectively. PD is the Node B available power, N0 is the noise spectral density, α is the intra-cell nonorthogonality coefficient and ε is the extra-cell interference power-to-the total received power ratio. Note that all powers must be converted to Watts and cannot be left as dBs. PT X can be found quite simply, by assuming that all of Node B’s power will be used during transmissions. Node B’s power is therefore divided amongst each of the transmission codes. If one assumes that 15 codes are used for transmission, then PT X becomes: PT X = PD 15 [W]. (18) Hence RX W/Rb PT X GP Eb c = P P Extra Inter I0 )PD + N0 W + εGc (αGc (19) per CDMA code. From [2], one can derive that the channel gain for the transmission, the intra-cell interference, and the extra-cell interference are given by: Gc(dB) = −L(t)(dB) + s(t)(dB) , [dBW] (20) where s(t) is the shadowing component, which will be discussed later. L(t) is the path-loss component of a UHF signal over flat terrain (in a semi-urban environment), which is given by [2]: L(t)(dB) = K + γ log10 r dBW. (21) Here K is the signal strength measured at 1km from the basestation, with r in km, while γ is the rate at which L(t)(dB) changes as r changes. Both K and γ may vary as distance r between a mobile and the base-station changes. The shadowing term s(t) (in dB) is usually modeled as a zero-mean stationary Gaussian process, which when expressed in Watts has a log-normal pdf: £ ¤ 1 · exp −(ln t − µ)2 /(2σ 2 ) (22) fs(t) = √ σ 2πt ¸ · 1 ln2 t = √ (23) · exp − 2 , 2σ σ 2πt since µ = 0. V. HSDPA TRANSMISSION RATES When a packet is selected for transmission to the j-th user, the bit-error-rate (BER) plays an important role. The applications using the various classes of service have different sensitivities to the BER. The BER thresholds that were used in this paper are 9.6 · 10−6 for video and voice traffic, and 8.4·10−7 for web traffic, as listed in Table II in Section VII. These values were chosen to correspond to a packet-error-rate (PER) proposed in [1] of 10−1 for video traffic and a mean PER of 10−2 for web traffic, which varies as the packet length of the web packets vary. The BER of the voice traffic is kept the same as video, TABLE I HSDPA MCS MODES MCS mode m 1. QPSK, rate 14 2. QPSK, rate 12 3. QPSK, rate 34 4. 16QAM, rate 21 5. 16QAM, rate 34 Data rate (15 codes) Rb 1.8Mbps 3.6Mbps 5.3Mbps 7.2Mbps 10.7Mbps but corresponds to a much lower PER, as can be calculated using the following expression: P ER = 1 − (1 − BER)L , (24) where L in this context is the packet length, measured in bits. The BER can be controlled directly by varying the transmission power. In the 3GPP’s UMTS standard, power control therefore plays an important role. One of the improvements that HSDPA offers, is to track a BER by varying the modulation and coding scheme (MCS) as the channel conditions change. This enables the base station to keep its transmission power constant, simplifying the design. In order to obtain high transmission rates, 15 codes were used with the MCS modes listed in Table I. As the channel quality varies, a different MCS will have to be used, which effectively varies the transmission rate. To determine which of these MCS modes should be used at any time, the indirect relationship between BER and transmission rate needs to be considered. Note that the BER is related to the bit-energy-to-interference spectral density EI0b value, which is communicated from the User Equipment (UE) to the transmitting Node-B using the Channel Quality Indicator (CQI) value [3]. This 5-bit CQI value, ranging from 0 to 30, is calculated as follows: Eb 0 j k if I0 ≤ −16, CQI = Eb I0 30 /1.02 + 16.62 if − 16 < if 14 < Eb I0 Eb I0 . ≤ 14, (25) When the CQI value is received by Node-B, it can be converted back to an estimate of EI0b . In the previous section, the following expression was developed that explains the relationship between the transmission rate Rb and the resulting EI0b that can be expected: RX Eb W/Rb PT X GP c (26) = P P Inter I0 + εGc Extra )PD + N0 W (αGc By choosing the appropriate MCS value, the transmission rate can be increased while the resulting EI0b still meets the following condition: µ ¶ Eb Eb > . (27) I0 I0 Threshold ³ ´ is a threshold that is directly related to the The EI0b Threshold maximum PER threshold that each class of traffic can endure. Reference [4] contains the required SNR-to-PER curves in a flat fading Rayleigh model for QPSK rate 1/3, rate 1/2, rate 3/4, 16QAM rate 1/2, and rate 3/4. VI. S CHEDULERS TO COMPARE D. Earliest Deadline First The behaviour of the following schedulers is explored in this paper: A. Round Robin [3] The Round Robin (RR) scheduler is one of the simplest. At the end of each TTI, it moves onto the next queue and tries to fill up the scheduling interval with as much data as possible from this queue. If a queue is empty, the scheduler simply moves to the next queue. No attempt at optimisation is made. If all queues are full, then over a prolonged period of time, each one would be given an equal number of TTIs for transmission. B. Proportional Fair Throughput [3] The aim of the Proportional Fair Throughput (PF-T) scheduler is to maximise throughput, but in a fair manner. The scheduling rule is given by: Next packet = max i ri , r̄i (28) The Earliest Deadline First (EDF) scheduler attempts to meet the required deadlines of traffic classes. Distributing bandwidth and achieving fair throughput among the traffic classes is not a sufficient criterion to be able to sustain realtime traffic on a packetised network. Strict deadlines need to be adhered to. The scheduling rule for EDF is given by: Next packet = min(di − Di ), i (32) where Di is the queueing delay that the head-of-queue packet of the class i queue has experienced, while di is the delay deadline of the packet. E. O-EDF Scheduler Finally, Opportunistic EDF (O-EDF) uses the rules of EDF, but prioritises traffic classes which will be transmitted through good channel conditions. Next packet = min di · i Ḡc − Di Gc (33) VII. S IMULATION PARAMETERS Table II lists the parameters for the simulation, which are similar to those proposed in [1]. A variable number of traffic where ri is the instantaneous transmission rate that HSDPA sources were modeled, depending on the required load. Each could assign to class i traffic, if chosen by the scheduler. r̄i traffic source generated the video, voice, and web traffic is the average transmission rate that was assigned to class i according to the model discussed in Section II. Traffic was traffic. It can be found as follows: generated independently of other traffic sources. ( For the HSDPA links between Node B and the UEs, (1 − α)r̄i (k) + αri (k) if i is served in slot k, r̄i (k + 1) = 20 mobile channels were created. Each packet that was (1 − α)r̄i (k) otherwise, (29) generated was assigned a flat random number ranging from 0 to 19, which implied the target channel that it would be where 0 < α < 1 is a weighting factor, whose value was transmitted through. Each of the 20 channels was modeled independently of all other channels. No spacial correlation chosen to be 0.001. The numerator in the scheduling rule ensures that the among transmission channels was taken into consideration. scheduler will take advantage of temporary throughput imVIII. R ESULTS provements. On the other hand, the denominator ensures that In this paper, the behaviour of five schedulers was simuover a long-term period, the scheduler will attempt to assign lated. equal resources to all classes. A. Average delay and unfairness C. Maximum Carrier to Interference ratio [3] The Maximum Carrier to Interference ratio (Max C/I) scheduler selects packets based purely on the best channel conditions. The relative instantaneous channel quality indicator η is defined by ηi = ζi , ζ̄ (30) where ζi = EI0b is the SNR of the channel that the head-ofqueue packet ofP the class i queue will be transmitted through, n while ζ̄ = n1 · j=1 ζj is the average SNR of all channels. Here n is the total number of traffic classes. The scheduling rule is simply given by: Next packet = max ηi . i (31) Figs. 1(a), 1(c), and 1(e) contain the average queueing delay that video, voice and web traffic respectively experience. At first glance, EDF seems to perform the least favourable, as of all three classes it has the highest queueing delay for large portions of the load conditions. One needs to remember though that EDF schedules traffic based on the delay deadlines of the classes, which are 100ms for video traffic, 50ms for voice traffic, and 500ms for web traffic. In the case of voice traffic, EDF traffic does not reach the deadline, but is still much higher than the other schedulers under high load conditions. The reason for voice delays not reaching the deadline is due to multiple transmissions of voice packets per TTI. The delay of video and web traffic, on the other hand, approaches the deadline at relatively low loads. The advantage of the EDF scheduler is the clear queueing delay differentiation of traffic classes. The delay deadlines of the various traffic classes create behavioral expectations with customers, which they are prepared to pay for. If no discernable difference is noticeable, the requirement for an advanced delay-targeting QoS design becomes questionable. To further illustrate the point, a form of delay unfairness is proposed that is defined as follows: Unfairness = 100% · di − Di , di (34) where di is the delay deadline of class i and Di is the queueing delay that the front-of-queue-i packet has experienced by the time it is served. In other words, with this expression an attempt is made to measure how close the delay was to its deadline, by the time the packet completed transmission. Figs. 1(b), 1(d), and 1(f) contain the average unfairness, measured according to (34). This highlights, more clearly, how EDF is able to differentiate the delay of the 3 classes, based on their respective deadlines. For all 3 classes, as the load increases, EDF approaches the deadline, marked as 0 on the unfairness graphs. In the case of video traffic, all schedulers are able to approach the delay deadline quite well. For most of the schedulers this is due to their preference of voice over video traffic. As the video delay approaches the delay deadline, it is dropped. This creates an artificial ceiling around the delay deadline. In the case of voice and web traffic, all schedulers, except for EDF, completely ignore the delay deadline. IX. C ONCLUSION This paper presented an HSDPA simulation environment. Video, voice, and web traffic were realistically produced by a varying number of sources. The number of sources was varied from 1 to 20, thereby achieving load-conditions varying from 0% to 100%. The model focussed on the downlink transmissions from the base-station to the mobile units, migrating around the cell. It measured the average queueing delay and fairness. The results show how EDF is able to differentiate between traffic classes. TABLE II S IMULATION PARAMETERS [1] PARAMETER α PD Cell radius R N = N0 W W ε (close to Node B: R ≤ 10m) ε (intermediate: 10m < R < 450m) ε (cell border: 450m ≤ R ≤ 500m) PATHLOSS ATTENUATION L(t) Near zone (R < 300m) Far zone (R ≥ 300m) None-line of sight (probability = 0.2) (R = distance from Node B) SHADOWING ATTENUATION s(t) (Gaussian variable) Mean Standard deviation Bit Error Rate Threshold Video Voice Web VALUE 0.025 30 Watt 500 meters 2.00245 · 10−14 Watt 5MHz 0 0.25 0.5 (in dB) 92.92 + 10.96 log(R) 106.48 + 43.85 log(R) 151.32 + log(R) (in dB) 0 8 9 · 10−6 9 · 10−6 8.4 · 10−7 R EFERENCES [1] F. D. Angelis, I. Habib, G. Giambene, and S. Giannetti, “Scheduling for differentiated traffic types in HSDPA cellular systems,” in IEEE Globecom ’05, 2005. [2] A. Farrokh, F. Blomer, and V. Krishnamurthy, “A comparison of opportunistic scheduling algorithms for streaming media in high-speed downlink packet access (HSDPA),” in Proc. of MIPS 2004, Grenoble, France, November 16–19, 2004. [3] A. Haider and R. Harris, “A novel proportional scheduling algorithm for HSDPA in UMTS networks,” in The 2nd International Conference on Wireless Broadband and Ultra Wideband Communications (I. C. Society, ed.), 2007. [4] A. Harada, S. Abeta, and M. Sawahashi, “Adaptive radio parameter control considering QoS for forward link OFCDM wireless access,” in IEEE VTC, pp. 1175–1179, 2002. Stefan M. Scriba completed his BScEng degree in December 2000 in the School of Electrical, Electronic and Computer Engineering at the University of Natal, Durban, South Africa. He completed his MScEng degree at the beginning of 2003 and is currently working on his PhD, researching scheduling theory for both wired and wireless networks in the Radio Access Technology Research Centre at the University of KwaZulu-Natal, South Africa. Since 2006 he is working at Telkom SA Ltd. Professor Fambirai Takawira is the Telkom Professor of Digital Communications at the School of Electrical, Electronic and Computer Engineering at the University of KwaZulu-Natal. He completed his BScElecEng in Manchester and was awarded a PhD at Cambridge. His research interests are in the general areas of adaptive signal processing, digital communications and data networks. 120 100 0.1 RR PF−T Max C/I EDF OEDF 0.08 Unfairness (%) Delay (ms) 80 60 40 0.06 0.04 0.02 20 0 0 0 5 10 Number of users 15 RR PF−T Max C/I EDF OEDF −0.02 0 20 5 (a) Average delay of Video traffic 10 Number of users 15 20 15 20 15 20 (b) Unfairness of Video traffic 25 20 0.1 RR PF−T Max C/I EDF OEDF 0.095 0.09 Unfairness (%) Delay (ms) 0.085 15 10 0.08 0.075 0.07 0.065 5 0.06 0.055 0 0 5 10 Number of users 15 RR PF−T Max C/I EDF OEDF 0.05 0 20 5 (c) Average delay of Voice traffic (d) Unfairness of Voice traffic 600 500 0.1 RR PF−T Max C/I EDF OEDF 0.08 Unfairness (%) Delay (ms) 400 300 200 0.06 0.04 0.02 100 0 0 0 5 10 Number of users 15 20 RR PF−T Max C/I EDF OEDF −0.02 0 (e) Average delay of Web traffic 5 10 Number of users (f) Unfairness of Web traffic Fig. 1. View publication stats 10 Number of users Average delay and unfairness